Osteoporosis diagnostic support apparatus

ABSTRACT

Provided is an apparatus for measuring the thickness, roughness, and morphology index of the mandibular cortical bone using a dental panorama image to assist in the diagnosis of osteoporosis, wherein the thickness, roughness, and morphological index of the cortical bone is measured more accurately and the diagnosis of osteoporosis can be supported more accurately. An osteoporosis diagnostic support apparatus, wherein the apparatus has a contour extraction unit adapted to extract a mandibular contour from an image of a mandibular cortical bone photographed by a photographic apparatus adapted to photograph the mandibular cortical bone and surroundings thereof, a line segment extraction unit adapted to extract line segments from the image of the mandibular cortical bone photographed by the photographic apparatus; and a cortical bone thickness calculation unit adapted to calculate a thickness of the cortical bone based on the extracted mandibular contour and line segments.

INCORPORATION BY REFERENCE TO RELATED APPLICATIONS

This application is a division of U.S. application Ser. No. 15/162,447,filed May 23, 2016, which is a continuation-in-part of PCT ApplicationNo. PCT/JP2014/081056, filed Nov. 25, 2014, which claims priority to JP2013-242678, filed Nov. 25, 2013. The above applications are herebyincorporated herein by reference in their entirety and are to beconsidered a part of this specification.

BACKGROUND Technical Field

The present invention relates to an apparatus which supportsosteoporotic diagnosis using X-ray photographs, and more particularly,to an apparatus which supports osteoporotic diagnosis by measuring athickness, a coarseness, and/or a morphological index of the mandibularcortical bone using a dental panoramic X-ray photograph (hereinafterabbreviated to a panoramic image).

Description of Related Art

In the field of dental treatment, it is a widespread practice to shoot apanoramic image covering an entire area of a tooth portion at the startof treatment. In so doing, not only the tooth portion, but also theupper and lower jawbones are photographed. In recent years, of the shotimages, images of a lower jawbone portion have come to be used tosupport osteoporotic diagnosis.

For example, Patent Literature 1 discloses a technical idea ofsupporting osteoporotic diagnosis by semiautomatically determining froma dental panoramic image whether an inner surface of a cortical boneportion of the lower jawbone is structured smoothly or coarsely.

Also, Patent Literature 2 discloses a technical idea of supportingosteoporotic diagnosis by measuring a thickness of a cortical boneportion of the lower jawbone from a dental panoramic image and comparingthe thickness of the cortical bone with data accumulated in anosteoporosis database.

Also, Patent Literature 3 discloses a technical idea of supportingosteoporotic diagnosis by detecting a mandibular contour in a dentalpanoramic image and comparing a thickness of the mandibular corticalbone, in particular, with stored contour model data.

Furthermore, Non Patent Literature 1 discloses a technique forautomatically measuring a thickness of the mandibular cortical boneusing a dental panoramic image, and more particularly, a technical ideaof acquiring a gray value profile of perpendicular lines from amandibular contour and measuring the thickness of the mandibularcortical bone based on the mandibular contour.

Also, Patent Literature 4 discloses a technical idea of supportingosteoporotic diagnosis by automatically identifying an area where thereis a change in bone density using a dental panoramic X-ray image.

CITATION LIST Patent Literature

-   Patent Literature 1: Japanese Patent No. 3964795-   Patent Literature 2: Japanese Patent No. 4956745-   Patent Literature 3: International Publication No. 2012/128121-   Patent Literature 4: Japanese Patent Laid-Open No. 2013-116293

Non Patent Literature

-   Non Patent Literature 1: Takuya Matsumoto, et al. “Osteoporosis    screening by use of automated scheme for measuring mandibular    cortical thickness on dental panoramic radiographs,” The Institute    of Electronics, Information and Communication Engineers, 2012    January.-   Non Patent Literature 2: Yukiyasu Yoshinaga, et al. “Evaluation    Method of Concentration Degree and Convergence Index Filter,”    MEDICAL IMAGING TECHNOLOGY, Vol. 19, No. 3, May 2001, issued by The    Japanese Society of Medical Imaging Technology. All of the above    patent and non-patent literature are incorporated herein by    reference in their entirety.

SUMMARY

However, a diagnostic support method of Patent Literature 1 has problemsin that the method has insufficient measurement accuracy and complicatedmeasuring means.

Also, a diagnostic support method of Patent Literature 2 has problems inthat means of establishing outer and inner edges of the mandibularcortical bone is complicated and is low in accuracy.

Note that further improvement in accuracy is expected also from methodsof Patent Literature 3 and Non Patent Literature 1 if there is acoarsely structured portion when the thickness of the mandibularcortical bone is measured.

Furthermore, the automatic osteoporosis diagnostic support method ofPatent Literature 4 has problems in that it is difficult to use imagesshot by different apparatus, making it necessary to determine whether ornot osteoporosis is suspected only by using images shot by a sameapparatus and that it is not possible to automatically identify anosteoporotic morphological index (type I, type II, or type III)effective in making an osteoporotic diagnosis and classify symptoms orquantitatively show suspicion of osteoporosis.

Thus, to solve the above problems, an object of the present invention isto provide an apparatus which supports osteoporotic diagnosis bymeasuring a thickness, a coarseness, and/or a morphological index of themandibular cortical bone using a dental panoramic image, wherein theapparatus can more accurately support osteoporotic diagnosis by moreaccurately measuring the thickness, coarseness, and/or morphologicalindex of the mandibular cortical bone.

To achieve the above object, the present invention provides anosteoporosis diagnostic support apparatus comprising:

a contour extraction unit adapted to extract a mandibular contour froman image of a mandibular cortical bone photographed by a photographicapparatus adapted to photograph the mandibular cortical bone andsurroundings thereof;

a line segment extraction unit adapted to extract line segments from theimage of the mandibular cortical bone photographed by the photographicapparatus; and

a cortical bone condition calculation unit adapted to calculate acondition of the cortical bone based on the extracted mandibular contourand line segments.

According to one aspect, in the osteoporosis diagnostic supportapparatus with the above configuration, the cortical bone conditioncalculation unit may be a cortical bone thickness calculation unitadapted to calculate a thickness of the cortical bone.

Furthermore, according to an aspect of the present invention, thecortical bone thickness calculation unit may be configured to calculatethe thickness of the cortical bone based on the line segments of thecortical bone extracted by the line segment extraction unit.

In particular, according to an aspect of the present invention, in theosteoporosis diagnostic support apparatus with the above configuration,the cortical bone thickness calculation unit may be configured tocalculate the thickness of the cortical bone based on the line segmentsof the cortical bone extracted by the line segment extraction unit.

Consequently, if position which satisfies a predetermined conditionwithin a range of a predetermined distance from the extracted linesegments are established as an inner edge of the cortical bone, thethickness of the cortical bone can be measured accurately by eliminatingthe influence of noise and the like, which helps greatly in providingsupport for osteoporotic diagnosis.

Furthermore, according to an aspect of the present invention, in theosteoporosis diagnostic support apparatus with the above configuration,the cortical bone thickness calculation unit may be configured tocalculate the thickness of the cortical bone based on the line segmentsof the cortical bone and line segments of a coarsely structured portionextracted by the line segment extraction unit.

Consequently, even if there are line segments in the coarsely structuredportion, the inner edge of the cortical bone can be establishedaccurately and the thickness of the cortical bone can be measured withhigh accuracy.

Alternatively, according to an aspect of the present invention, in theosteoporosis diagnostic support apparatus with the above configuration,the cortical bone condition calculation unit may be a cortical bonecoarseness calculation unit adapted to calculate a coarseness of thecortical bone.

Furthermore, according to an aspect of the present invention, in theosteoporosis diagnostic support apparatus with the above configuration,the cortical bone coarseness calculation unit may be configured tocalculate a coarseness of the cortical bone based on the line segmentsof the coarsely structured portion extracted by the line segmentextraction unit.

This allows osteoporotic diagnosis to be supported easily due tolargeness of the number or area of the extracted line segments in thecoarsely structured portion.

Also, according to an aspect of the present invention, in theosteoporosis diagnostic support apparatus with the above configuration,a line-convergence index filter is used as the line segment extractionunit. This allows the line segments to be extracted easily andaccurately.

Also, according to an aspect of the present invention, in theosteoporosis diagnostic support apparatus with the above configuration,determination of a measurement reference point in the cortical bonecondition calculation unit includes detecting a mandibular angle. Thisallows the measurement reference point to be determined accurately by asimple and easy method.

Note that according to another aspect, the present invention may beimplemented as an osteoporosis diagnostic support program configured tomake a computer function as:

contour extraction means for extracting a mandibular contour from animage of a mandibular cortical bone photographed by a photographicapparatus adapted to photograph the mandibular cortical bone andsurroundings thereof;

line segment extraction means for extracting line segments from theimage of the mandibular cortical bone photographed by the photographicapparatus; and

cortical bone condition calculation means for calculating a condition ofthe cortical bone based on the extracted mandibular contour and linesegments, where the contour extraction means, the line segmentextraction means, and the cortical bone condition calculation meanscorrespond to the contour extraction unit, the line segment extractionunit, and the cortical bone condition calculation unit in the abovedescription, respectively.

This allows the present invention to be implemented by a programregardless of the configuration of the apparatus.

Likewise, according to another aspect, the present invention may beimplemented as an osteoporosis diagnostic support program configured tomake a computer function as cortical bone thickness calculation meansand cortical bone coarseness calculation means, which correspond,respectively, to the cortical bone thickness calculation unit and thecortical bone coarseness calculation unit in the above description.

Also, according to another aspect of the present invention, there isprovided an osteoporosis diagnostic support apparatus comprising: acontour extraction unit adapted to extract a mandibular contour from animage of a mandibular cortical bone photographed by a photographicapparatus adapted to photograph the mandibular cortical bone andsurroundings thereof; a line segment extraction unit adapted to extractline segments from the image of the mandibular cortical bonephotographed by the photographic apparatus, where the line segments areformed by a gray level distribution and include line segments of thecortical bone and line segments of a coarsely structured portion; and amandibular cortical bone morphological index identification unit adaptedto extract a feature value based on at least one of the extractedmandibular contour and line segments and identify a mandibular corticalbone morphological index by the feature value.

This makes it possible to identify type I to type III below, whichbelongs to the morphological index of the mandibular cortical bone foruse to support osteoporotic diagnosis. Type I is characterized by smoothinside surfaces of the cortical bone on both sides, Type II ischaracterized by irregular inside surfaces of the cortical bone and inthat linear absorption is observed in a neighborhood of an inner sideinside the cortical bone, and Type III is characterized in that advancedlinear absorption as well as fractures of the cortical bone are observedover the entire cortical bone.

The feature value may include one or more of:

a feature value of the thickness of the cortical bone,

the number of pixels of line elements in a cortical bone regionestimated to be dense when regions classified by density are estimatedbased on the extracted mandibular contour and line segments,

the number of pixels of line elements in a cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity,

area of the cortical bone region estimated to be coarse in theestimation of the regions classified by density,

a ratio of average concentration value of line elements between thecortical bone region estimated to be dense and the cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity,

0-, 45-, 90-, or 135-degree variance of the cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity,

0-, 45-, 90-, or 135-degree difference variance of the cortical boneregion estimated to be coarse in the estimation of the regionsclassified by density,

45-, 90-, or 135-degree difference entropy of the cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity,

0-degree inverse difference moment of all cortical bone regionsestimated to be dense or coarse in the estimation of the regionsclassified by density,

0-degree difference entropy of all the cortical bone regions estimatedto be dense or coarse in the estimation of the regions classified bydensity, and

0-degree difference variance of all the cortical bone regions estimatedto be dense or coarse in the estimation of the regions classified bydensity.

Consequently, feature values useful in providing support forosteoporotic diagnosis are used selectively or in combination, amorphological index can be found with high accuracy.

Note that the mandibular cortical bone morphological indexidentification unit may be an identification unit made up of a supportvector machine. This enables efficient identification even when a largenumber of feature values are used for identification.

Furthermore, the mandibular cortical bone morphological indexidentification unit may have a bone density estimation function. Use ofthe feature values described so far makes it possible to quantitativelyestimate bone density and thereby support appropriate osteoporoticdiagnosis.

Note that according to another aspect, the present invention may beimplemented as an osteoporosis diagnostic support program configured tomake a computer function as:

contour extraction means for extracting a mandibular contour from animage of a mandibular cortical bone photographed by a photographicapparatus adapted to photograph the mandibular cortical bone andsurroundings thereof;

line segment extraction means for extracting line segments from theimage of the mandibular cortical bone photographed by the photographicapparatus; and

a mandibular cortical bone morphological index identification means forextracting a feature value based on at least one of the extractedmandibular contour and line segments and identifying a mandibularcortical bone morphological index by the feature value, where thecontour extraction means, the line segment extraction means, and themandibular cortical bone morphological index identification means, whichcorrespond, respectively, to the contour extraction unit, line segmentextraction unit, and the mandibular cortical bone morphological indexidentification unit in the above description.

This allows the present invention to be implemented by a programregardless of the configuration of the apparatus.

The osteoporosis diagnostic support apparatus according to the presentinvention obtains information useful for osteoporotic diagnosis fromimages of the mandibular cortical bone and achieves great effects indiagnostic support. In particular, the aspect of the invention describedabove can accurately calculate a cortical bone thickness regardless ofthe presence or absence of a coarsely structured portion, orquantitatively calculate a coarseness of coarsely structured portions,making it easy to support osteoporotic diagnosis. Also, another aspectof the invention, makes it possible to obtain the osteoporoticmorphological index (type I, type II, or type III) useful in supportingosteoporotic diagnosis, which helps in providing support forosteoporotic diagnosis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an osteoporosis diagnostic supportapparatus according to a first embodiment of the present invention.

FIG. 2 is a flowchart of the osteoporosis diagnostic support apparatusaccording to the first embodiment of the present invention.

FIG. 3 is an example of an image obtained when a result of extracting amandibular contour by the osteoporosis diagnostic support apparatusaccording to the first embodiment of the present invention is displayedin overlays on a panoramic image.

FIG. 4 is an explanatory diagram of measurement reference points on theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIG. 5 is an explanatory diagram of image creation on the osteoporosisdiagnostic support apparatus according to the first embodiment of thepresent invention.

FIG. 6 is an explanatory diagram of profile acquisition on theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIGS. 7A-7C are images created using profiles of the osteoporosisdiagnostic support apparatus according to the first embodiment of thepresent invention.

FIG. 8 is an explanatory diagram showing an outline of aline-convergence index filter of the osteoporosis diagnostic supportapparatus according to the first embodiment of the present invention.

FIG. 9 is an explanatory diagram of search lines on the osteoporosisdiagnostic support apparatus according to the first embodiment of thepresent invention.

FIG. 10 is an image showing results of line-convergence index filteringon the osteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIG. 11 is an image showing results of ridge line extraction on theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIG. 12 is an image obtained before ridge line selection on theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIG. 13 is an image obtained after ridge line selection on theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIG. 14 is an explanatory diagram of an appropriate search range on theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIG. 15 is an explanatory diagram showing an effect of the presence orabsence of a ridge line of coarse structure on measurement results ofthe osteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

FIGS. 16A-16B are explanatory diagrams showing a decrease width of grayvalue on the osteoporosis diagnostic support apparatus according to thefirst embodiment of the present invention.

FIG. 17 is a flowchart of the osteoporosis diagnostic support apparatusaccording to the first embodiment of the present invention.

FIGS. 18A-18B are images obtained before and after application of aline-convergence index filter of the osteoporosis diagnostic supportapparatus according to the first embodiment of the present invention.

FIGS. 19A-19B are result images of the osteoporosis diagnostic supportapparatus according to the first embodiment of the present invention.

FIG. 20 is a block diagram of an osteoporosis diagnostic supportapparatus according to a second embodiment of the present invention.

FIG. 21 is a flowchart of the osteoporosis diagnostic support apparatusaccording to the second embodiment of the present invention.

FIG. 22 is an explanatory diagram of a region of interest on theosteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIG. 23 is an explanatory diagram of linear structures on theosteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIGS. 24A-24B are explanatory diagrams of profile acquisition on theosteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIG. 25 is an explanatory diagram of region estimation on theosteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIG. 26 is an explanatory diagram showing results of region estimationon the osteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIG. 27 is an explanatory diagram showing measurement reference pointson the osteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIG. 28 is an explanatory diagram showing feature values of theosteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

FIG. 29 is an explanatory diagram of cases on the osteoporosisdiagnostic support apparatus according to the second embodiment of thepresent invention.

DETAILED DESCRIPTION

An osteoporosis diagnostic support apparatus according to a firstembodiment of the present invention is described below with reference tothe drawings. Note that description is given below schematically to theextent necessary to achieve the object of the present invention, thatdescription is given mainly to the extent necessary to describeappropriate parts of the present invention, and that description relatedto known techniques is omitted.

FIG. 1 is a block diagram of the osteoporosis diagnostic supportapparatus according to the first embodiment of the present invention. Asshown in FIG. 1 , the osteoporosis diagnostic support apparatus 1includes a photographic apparatus 10 adapted to shoot subject images ofpatients or the like, an image analysis apparatus 20 adapted to analyzeimages shot by the photographic apparatus 10, and a display apparatus 70adapted to display the images shot by the photographic apparatus 10 andinformation obtained by the image analysis apparatus 20, where theseapparatus are linked together by wired and/or wireless connections.

The image analysis apparatus 20 includes a CPU 30, a memory 40, andinterfaces 50 and 60, which are linked, for example, as shown in FIG. 1. The memory 40 includes a contour extraction unit 41, a cortical bonethickness calculation unit 42, a cortical bone coarseness calculationunit 43, a line segment extraction unit 44, and an osteoporosisdiagnostic support unit 45.

A panoramic X-ray imaging apparatus, which is a type of the photographicapparatus 10, is an apparatus adapted to shoot panoramic images in thedental area with X-rays. Various types of panoramic X-ray imagingapparatus have been put to practical use and any of them may be adopted.Note that the photographic apparatus 10 is not limited to the panoramicX-ray imaging apparatus, and any of a usual X-ray imaging apparatus,MRI/CT imaging apparatus, ultrasound imaging apparatus, or a combinationthereof may be adopted as the photographic apparatus 10. Appropriatediagnostic support may be provided by resulting images.

The panoramic image shot by the panoramic X-ray imaging apparatusserving as the photographic apparatus 10 is sent to the image analysisapparatus 20. The image analysis apparatus 20 analyzes images, beingprovided with computer resources including at least the CPU 30, memory40, interface 50 with the photographic apparatus 10, and interface 60with the display apparatus 70 (described later). The computer resourcesmay be provided in the form of a server or personal computer installedin close proximity, similar apparatus linked by wired and/or wirelessconnections, or Internet-based cloud.

The display apparatus 70 is connected to the image analysis apparatus 20via the interface 60 and is capable of displaying the images shot by thephotographic apparatus 10, images of a mandibular contour and linesegments extracted by the image analysis apparatus 20, information abouta thickness, a coarseness, and the like of the cortical bone calculatedby the image analysis apparatus 20, osteoporosis diagnostic supportinformation obtained by the image analysis apparatus 20, and the like.

The contour extraction unit 41 is provided as a program stored in thememory 40 of the image analysis apparatus 20. The contour extractionunit 41 extracts a mandibular contour from a panoramic image. Themandibular contour is a portion which defines an outer edge of the lowerjawbone.

Also, the cortical bone thickness calculation unit 42, which is one ofcortical bone condition calculation units, is provided as part of theimage analysis apparatus 20. The cortical bone thickness calculationunit 42 is a program stored in the memory, and is capable of causing acomputer to perform a function to calculate a thickness of the corticalbone from a panoramic image.

Furthermore, the cortical bone coarseness calculation unit 43, which isone of the cortical bone condition calculation units, is provided aspart of the image analysis apparatus 20. The cortical bone coarsenesscalculation unit 43 is a program stored in the memory, and is capable ofcausing a computer to perform a function to calculate a coarseness ofthe cortical bone from a panoramic image.

Besides, the line segment extraction unit 44 is provided as part of theimage analysis apparatus 20. The line segment extraction unit 44, whichis, for example, like a line-convergence index filter, is a programstored in the memory, and is capable of causing a computer to perform afunction to extract line segments from a panoramic image. The linesegment extraction unit 44 is used as part of the cortical bonethickness calculation unit 42 and also as part of the cortical bonecoarseness calculation unit 43.

Note that any one or both of the cortical bone thickness calculationunit 42 and cortical bone coarseness calculation unit 43 may beprovided.

Furthermore, the osteoporosis diagnostic support unit 45 is provided aspart of the image analysis apparatus 20, allowing calculation resultsproduced by the cortical bone thickness calculation unit 42 and corticalbone coarseness calculation unit 43 to be compared with the data storedin an osteoporosis diagnostic support database (not shown), which ispart of the osteoporosis diagnostic support unit 45.

Now, operation of the osteoporosis diagnostic support apparatusconfigured as described above is described. FIG. 2 is a flowchart of theosteoporosis diagnostic support apparatus according to the firstembodiment of the present invention.

<Image Shooting> (S10)

First, images of the lower jawbone and surroundings thereof are shot bya panoramic X-ray imaging apparatus, which is a type of the photographicapparatus 10.

<Contour Extraction> (S20)

Next, the shot dental panoramic image is inputted to the image analysisapparatus 20, and the mandibular contour is extracted by the contourextraction unit 41, which is part of the image analysis apparatus 20.

Specifically, this is done as follows. First, edges are detected in theimage by Canny method. This is done by performing a) image smoothing, b)calculation of edge strength and direction, c) non-maximum suppression,and d) hysteresis thresholding in this order, and Kirsch's method whichis a template edge detection operator is used in conjunction to inhibitdetection of edges irrelevant to the lower jawbone.

Furthermore, to extract a mandibular contour as a more accurate linefrom the image subjected to edge extraction, a dynamic contour modelmethod is used. The techniques mentioned so far are described in detailin Patent Literature 3. FIG. 3 is an example of an image obtained when aresult of extracting a mandibular contour using these techniques isdisplayed in overlays on a panoramic image.

Next, description will be given of detailed operation of the corticalbone thickness calculation unit 42 which calculates a cortical bonethickness using the shot image and extracted contour. As shown in FIG. 2, the cortical bone thickness calculation unit 42 (related to step S30)includes a function to implement the following steps.

<Determination of measurement reference points> (S31)

<Acquisition of profiles> (S32)

<Ridge line extraction using line-convergence index filter> (S33)

<Profile group selection> (S34)

<Thickness measurement> (S35)

These steps are described in detail below. Note that the numeric valuescited in the description are desirable examples, but are notrestrictive, and that numeric values may be selected, as appropriate,according to conditions of the image or accuracy of diagnostic support.

<Determination of Measurement Reference Points>

Measurement reference points are found from a mandibular contour. Tocalculate a state (thickness or coarseness) of the lower jawbone, it isdesirable to establish measurement reference points near locationsimmediately under the foramen mentale on both right and left sides,which enable stable calculations.

FIG. 4 is an explanatory diagram of measurement reference points.Specifically, measurement reference points X_(L) and X_(R) on both sidesare found using the following technique.

a) Detect mandibular angles. The mandibular angles, which exist on bothright and left sides, correspond to spots at which angles formed bytangents to the mandibular contour and a perpendicular line become 15degrees or less for the first time.

b) A distance obtained by multiplying a distance Y between the right andleft mandibular angles by a predetermined coefficient is established asa distance between the right and left measurement reference points.Preferably the predetermined coefficient for positions corresponding tothe foramen mentale is set to 0.48 based on data accumulated so far, butthis is not restrictive.

c) The distance between the measurement reference points is divided atthe central part of the mandibular contour into right and left, and thetwo measurement reference points are denoted by X_(L) and X_(R).

Now, although it has been stated that the spots at which angles formedby tangents to the mandibular contour and a perpendicular line become 15degrees or less for the first time are used in detecting the mandibularangles, the angle used in detecting the mandibular angles is not limitedto 15 degrees or less, and may be 20 degrees or less. Also, it is notnecessary to detect the mandibular angles exactly, and spots close tothe mandibular angles and effective in determining measurement referencepoints may be used. Even in that case, the angle is not limited to 15degrees, and may be larger (e.g., 25 degrees) or smaller (e.g., 10degrees).

Note that the method for determining measurement reference points is notlimited to the above method, and may be a method which involvesdetecting the foramen mentale by enhancing light and shade such asdescribed in Patent Literature 1 or a method which involves determiningmeasurement reference points in comparison with a contour model databasewhich records positions corresponding to the foramen mentale such asdescribed in Patent Literature 3.

<Acquisition of Profiles>

Next, plural points are established at predetermined intervals aroundeach measurement reference point on the mandibular contour, aperpendicular line from each of the points to the mandibular contour isacquired, and gray values are found at predetermined intervals on theperpendicular line. Changes in the gray values are represented by whatis known as a profile.

Specifically, as shown in the explanatory diagram of image creation inFIG. 5 , images G₁ and G₂ 101 pixels wide along the mandibular contourand 100 pixels long in a vertical direction from the mandibular contourare created, respectively, around the right and left measurementreference points, where 1 pixel=0.1 mm.

The profile is acquired as follows. FIG. 6 is an explanatory diagram ofprofile acquisition.

First, a Gaussian filter is applied to the profile to remove noise fromthe profile. The Gaussian filter is given by the following Equation.

$\begin{matrix}{{f(x)} = {\frac{1}{\sqrt{2\pi}\sigma}{\exp\left( {- \frac{x^{2}}{2\sigma^{2}}} \right)}}} & \left\lbrack {{Mathematical}\mspace{14mu}{expression}\mspace{14mu} 1} \right\rbrack\end{matrix}$

The smaller the value of σ, the smaller the effect of smoothing whilethe larger the value, the larger the effect. Here, it is assumed thatσ=0.8, but the value may be changed according to image quality.

Note that the noise removal filter is not limited to the Gaussianfilter, and various filters are conceivable, including a moving averagemethod, a median filter, a bilateral filter, an anisotropic diffusionfilter, and a non-local means filter depending on the situation, any ofwhich may be applied.

Subsequent procedures are as follows.

a) Pixel values are determined on a total of 101 perpendicular linesfrom the right and left measurement reference points and 100 pointstherearound on the mandibular contour and converted into images. Thatis, an image is created as follows: the x_(i)th point from the left sideof the contour out of the points on the contour used and a pixel valueat a distance y_(i) (y_(i)=1 to 100) from a measurement start pointdetermined from the profile are converted into a pixel value atcoordinates (x_(i), y_(i)) on an image created in advance. The lowermostend of the image created corresponds to the mandibular contour or themeasurement start point of the profile.

b) Position of a peak on each of the acquired profiles is extracted, andthe pixel values at locations corresponding to the image are maximized,i.e., turned white.

Here, in the example of FIG. 6 , a lower left portion (b) of FIG. 6shows the profile at the first point X_(LL) from the left end as viewedfrom the measurement reference point X_(L) on the observers' left on themandibular contour, and it can be seen that gray value peeks exist atspots at distances of 28, 50, 61, 72, 89, and 98 pixels from themeasurement start point.

Also, a lower right portion (c) of FIG. 6 shows an extracted peak of aprofile. When relevance of the peak is observed in (c) portion of FIG. 6, it can be seen that gray value peeks of a coarse structure and thecortical bone desired to be extracted stretch continuously in ahorizontal direction of the image. Using the continuity of peaks onplural profiles, gray value peeks on a coarse structure can beidentified.

<Ridge Line Extraction Using Line-Convergence Index Filter>

Next, description will be given of a technique for applying aline-convergence index filter to an image created from a profile, toextract line segments (referred to as ridge lines) formed by a densitydistribution. Note that an outline of the technique is disclosed in NonPatent Literature 2.

Here, steps up to step a) described in relation to the profileacquisition are carried out. Note that no peak extraction is performed.That is, an image such as shown in each of FIGS. 7A to 7C is created asfollows: the x_(i)th point from the left side of the contour out of thepoints on the contour used and a pixel value at a distance y_(i)(y_(i)=1 to 100) from a measurement start point determined from theprofile are converted into a pixel value at coordinates (x_(i), y_(i))on a created image. The lowermost end of the image created correspondsto the mandibular contour or the measurement start point of the profile.

Then, to extract ridge lines (places where gray value peeks stretchcontinuously), a line-convergence index filter is applied to the imagecreated using a profile.

The line-convergence index filter is a line detection filter when astraight line is regarded as a target. FIG. 8 is an explanatory diagramshowing outline of a line-convergence index filter used for thistechnique, where central portion is the brightest and a region such asshown in (a) portion of FIG. 8 in which isophotes extend in parallelcorresponds to this, and the region is called a linear convex region. Adistribution of the intensity gradient vectors whose directions allconverge perpendicularly to a center line as shown in (b) portion ofFIG. 8 , is referred to as a line-convergence vector field anddesignated as a basic model. Also, the line on which the vectorsconverge is referred to as a vector-convergence line. Hereinafter, thedegree of vector convergence on a line will be referred to as a lineconvergence index.

FIG. 9 is an explanatory diagram of search lines used for thistechnique. A straight line 90 in a direction ϕ is considered, andassumed to be a vector-convergence line. A search line 91 and searchline 92 parallel to the straight line are considered, and rectangularregions with a width of W_(R) or W_(L) and length of 1 are considered onboth sides (R side and L side) of the straight line in a region enclosedby the search lines 91 and line 92. If θ_(ij) (ϕ) is an angle formed bythe intensity gradient vector of the jth pixel on a search line with adistance of i in the regions and a perpendicular line drawn therefrom tothe vector-convergence line, an evaluation value C_(R) on the R side canbe defined as follows.

$\begin{matrix}{{C_{R}(\varnothing)} = {\max\limits_{0 < w_{R} \leq w_{\max}}{\frac{1}{{lw}_{R}}{\sum\limits_{i = 1}^{w_{R}}\;{\sum\limits_{j = 0}^{l}\;{\cos\left( {\theta_{ij}(\varnothing)} \right)}}}}}} & \left\lbrack {{Mathematical}\mspace{14mu}{expression}\mspace{14mu} 2} \right\rbrack\end{matrix}$

where Wmax is the largest search width. The same applies to anevaluation value C_(L) on the L side. It is assumed that the lineconvergence index at a point of interest in the assumed direction ϕ isgiven by an average value of C_(R) and C_(L). Because an actualdirection of the vector-convergence line is unknown, the range in FIG. 9is divided according to the purpose, the convergence index in eachdirection is determined, and the largest value of the convergence indexis designated as an output C of the line-convergence index filter. Theline-convergence index filter is less susceptible to contrast with thebackground, and is adaptable to variation in line width as well. Whenthe point of interest is located on the vector-convergence line, i.e.,when luminance is placed on a third axis, output from the partcorresponding to the ridge line takes a maximum value of 1. Withincreasing distance from the vector-convergence line, the outputdecreases monotonously, and takes 0.5 in a peripheral portion.

When the maximum search width Wmax is made very small, the output of theline-convergence index filter takes 1 on the vector-convergence line,but decreases rapidly with increasing distance from thevector-convergence line. Using this property, processes corresponding tothinning and ridge line extraction can be implemented.

The line-convergence index filter is used to extract ridge lines from animage created using profiles. Then, by making the maximum search widthWmax for ridge line extraction very small, parameters of theline-convergence index filter are set as follows.

Maximum search width: Wmax=3 [pix].

Width: R=8, L=8.

Assumed directions: ϕ=0, 15, 30, 45, 135, 150, and 165 degrees.

These angles are preferable because the cortical bone and coarsestructure have a vector-convergence line in the horizontal directionwith respect to the lowermost end of the image (the lowermost end of theimage corresponds to the mandibular contour).

By setting the above-mentioned parameter, the line-convergence indexfilter is applied to the image of each of FIGS. 7A to 7C. FIG. 10 is anexplanatory diagram showing results of line-convergence index filtering,and it can be seen that portions corresponding to the ridges in theimage react strongly.

FIG. 11 is an explanatory diagram showing results of ridge lineextraction. Furthermore, to extract ridges as lines, when the output Cof the line-convergence index filter is 0.50 or less (i.e., θ_(ij)(ϕ)>45 degrees, where θ_(ij) (ϕ) is an angle from the perpendicular lineto the vector-convergence line), the output is assumed to be 0. Finally,lines to be ridge lines are extracted through thinning and verylow-value thresholding.

<Selection of Ridge Lines>

It is assumed that the ridge lines extracted by the application of aline-convergence index filter are classified into three types: ridgelines formed by gray value peaks of the cortical bone, ridge linesformed by gray value peaks of coarse structure, and noise. The lines onthe image subjected to ridge line extraction as shown in FIG. 11 areclassified by the following method.

Note that for simplicity, “the ridge lines formed by gray value peaks ofthe cortical bone” will be referred to as “ridge lines of the corticalbone” while “the ridge lines formed by gray value peaks of coarsestructure” will be referred to as “ridge lines of coarse structure.”

a) Noise Removal

Any line less than 15 pixels in width along an x-axis is judged to be anoise and is deleted from the image. The value of 15 pixels is usedbecause measurements are taken using 15 profiles in the end.

b) Ridge Line of Cortical Bone

After the noises are deleted, the line existing at the lowermost end ofthe image is designated as a ridge line of the cortical bone.

c) Ridge Line of Coarse Structure

It is estimated from sample data that a boundary between the corticalbone and cancellous bone in the profile exists at a spot between a grayvalue peak Ts and Ts+20 [pix]. Thus, in the image after noise deletion,any line located 20 pixels or more away from the line selected in b)along a y-axis is deleted as having no effect on thickness measurement.

Also, to prevent large dispersion from occurring in thicknessmeasurement values and credibility of thickness measurement from beinglost, any line overlapping the line selected in b) at less than 15coordinate points along the x-axis is deleted. The number of 15 is usedbecause 15 profiles are used in thickness measurement in the end. Notethat FIG. 12 and FIG. 13 are images before and after ridge lineselection.

<Selection of Most Optimum Profile Group>

Subsequently, a profile group considered to be most useful for thicknessmeasurement of the mandibular cortical bone is selected. If there is acoarse structure in the cortical bone region whose thickness ismeasured, gray value peeks of the cortical bone and coarse structure canbe selected from a large number of peaks existing in the acquiredprofiles using the ridge lines of the cortical bone and coarse structureextracted earlier. That is, as shown in the illustration of anappropriate search range in FIG. 14 , the start point Ts and end pointTe of a search range Z on the profile can be dynamically changed to anappropriate position on a profile by profile basis using the ridge lineL_(A) of the cortical bone and ridge line L_(B) of the coarse structure,making it possible to measure the thickness with high accuracy. Notethat in a right portion (b) of FIG. 14 , the abscissa represents thedistance [pix] from the measurement start point while the ordinaterepresents the gray value.

Furthermore, because the search range extends to the gray value peak ofthe coarse structure, a profile existing at a location where theboundary between the cortical bone and cancellous bone is clear can beselected.

Description will be given below of details of a method for dynamicallydetermining a search range and a method for selecting a profile groupbest suited for the thickness of the mandibular cortical bone.

a) Using only the profile containing an extracted ridge line of thecortical bone.

When there is an x-axis without any ridge line of the cortical bone, itis considered that the gray value peak of the cortical bone is notformed stably in the profile corresponding to the x-axis, and thus theprofile is not used because of unsuitability for thickness measurementof the cortical bone.

b) A method for determining a search range on a profile by profilebasis.

Only the profile corresponding to an image containing an extracted ridgeline of the cortical bone is used for thickness measurement. The methodfor determining a search range is determined as follows.

b-1) If the profile contains a spot corresponding to the ridge line ofcoarse structure, a search start point Ts and search end point Te areset at the gray value peak of the cortical bone and gray value peak ofthe coarse structure, respectively, using the determined ridge line.

b-2) If the profile does not contain any spot corresponding to the ridgeline of coarse structure, the search start point Ts is set at the grayvalue peak of the cortical bone and the search end point Te is set atTs+20 pixels, using the determined ridge line.

c) Selecting the best profiles.

To determine the 15 best adjacent profiles, candidates for the bestprofiles are narrowed down according to the presence or absence of aridge line of coarse structure. Then, a technique for determining the 15best adjacent profiles based on a decrease width of the search range isused. Details of the technique for selecting the best profiles are shownbelow.

c-1) Selecting candidates for the best profiles according to thepresence or absence of a ridge line of coarse structure.

When the image contains a ridge line of coarse structure, if a profilecontains a peek corresponding to a ridge line attributable to a grayvalue peak of coarse structure, the profile is included in a candidategroup for the best profiles and any profile without a peek is excludedfrom the candidates for the best profiles.

When the image does not contain a ridge line of coarse structure, allthe profiles containing any extracted gray value peak of the corticalbone are adopted as candidates without narrowing down candidates for thebest profiles.

FIG. 15 is an explanatory diagram showing an effect of the presence orabsence of a ridge line of coarse structure on measurement results. Inthickness measurement, measurement results R_(A) produced using aprofile P_(A) containing a ridge line of coarse structure are morestable than measurement results R_(B) produced using a profile P_(a)without any ridge line of coarse structure.

c-2) Determining the 15 best adjacent profiles using the decrease widthof the search range.

Finally, the decrease width between the gray value at the search startpoint Ts and the smallest gray value in the search range is determinedfrom the profile selected according to the procedures of “selectingcandidates for the best profiles according to the presence or absence ofa ridge line of coarse structure.” FIGS. 16A and 16B are explanatorydiagrams showing a decrease width of gray value, where the decreasewidth D between Ts and Te shown in FIG. 16A on the left representsclearness of the boundary between the cortical bone and cancellous boneas shown in FIG. 16B on the right and selecting a profile with a largedecrease width is tantamount to selecting a profile suitable forthickness measurement of the cortical bone. Thus, subsequent to theprocedures of “selecting candidates for the best profiles according tothe presence or absence of a ridge line of coarse structure,” a profilegroup which maximizes the sum total ΣD of decrease widths of 15 adjacentprofiles is further selected, thereby implementing the selection of thebest profiles.

<Measurement of Thickness of Cortical Bone>

Finally, thickness measurements are made using the selected profilegroup.

Using the 15 best adjacent profiles determined by the above technique aswell as a dynamic search range, the boundary between the cortical boneand cancellous bone is set based on a gradient of the profile asdescribed below, thereby measuring the thickness of the cortical bone.

The thickness measurement of the cortical bone based on the gradient ofthe profile involves determining the gradients (A₁, A₂, . . . , A₁₅) ofthe profile at each of points beginning with a measurement start pointand calculating an average Aave of only the gradients associated withdecreasing gray values. Next, a point Tresult closest to Ts whichsatisfies the condition of Ai<Aave is determined, and the distance fromthe measurement start point to Tresult is designated as the thickness ofthe cortical bone.

The thickness of the cortical bone is displayed on the display apparatus70, supporting a physician in making an osteoporotic diagnosis or iscompared with data stored in the osteoporosis diagnostic supportdatabase, which is part of the osteoporosis diagnostic support unit 45,making it possible to judge the progress of osteoporosis and supportosteoporotic diagnosis.

Next, detailed operation of the cortical bone coarseness calculationunit 43 will be described. As shown in FIG. 17 , the cortical bonecoarseness calculation unit 43 (related to step S40) includes a functionto implement the following steps.

<Determination of measurement reference points> (S41)

<Acquisition of profiles> (S42)

<Line segment extraction using line-convergence index filter> (S43)

<Line segment area calculation> (S44)

<Coarseness measurement> (S45)

These steps will be described in detail below. Note that the numericvalues cited in the description are desirable examples, but are notrestrictive, and that numeric values may be selected, as appropriate,according to conditions of the image or accuracy of diagnostic support.

A technique similar to that of the cortical bone thickness calculationunit 42 is used in steps up to determination of measurement referencepoints and acquisition of profiles.

<Line Segment Extraction Using Line-Convergence Index Filter>

In applying the line-convergence index filter, in the case of coarsenesscalculation, as with thickness calculation, when the output C of theline-convergence index filter is 0.50 or less, the output is assumed tobe C=0. Note that in the case of coarseness detection, changes can bemade, such as lowering the threshold, by taking conditions of the imageinto consideration.

<Line Segment Area Calculation>

FIGS. 18A and 18B are images obtained before and after application ofthe line-convergence index filter, where FIG. 18A on the left is animage before application while FIG. 18B on the right is an image afterapplication. These are digitized images obtained by setting C<0.50 toC=0, and C>=0.50 to C=1. After application of the line-convergence indexfilter, the sum total of positive pixels in the digitized image obtainedby using C=0.50 as a threshold is calculated. There are two ROIs(regions of interest), on the right and left, and thus the result may beobtained by taking an average of positive pixel counts on the right andleft or using the smaller of the positive pixel counts on the right andleft.

<Coarseness Measurement>

The degree of coarseness of the cortical bone is determined based onwhether the sum total of positive pixels is large or small. FIGS. 19Aand 19B are examples of result images, where FIG. 19A is an example inwhich there are lots of coarse structures and osteoporosis is suspectedwhile FIG. 19B is an example in which there is no coarse structure andthe patient is considered normal.

The coarseness information about this cortical bone is displayed on thedisplay apparatus 70, supporting a physician in making an osteoporoticdiagnosis or is compared with data stored in the osteoporosis diagnosticsupport database, which is part of the osteoporosis diagnostic supportunit 45, making it possible to judge the progress of osteoporosis andsupport osteoporotic diagnosis.

Note that the cortical bone thickness calculation unit 42 and corticalbone coarseness calculation unit 43 described so far may be usedseparately or may be used in combination.

Also, a cortical bone coarse structure calculation unit may be providedas the cortical bone condition calculation unit. Approximateosteoporotic diagnosis can also be supported based on whether there areline segments attributable to a coarse structure inside or outside thecortical bone.

Next, an osteoporosis diagnostic support apparatus according to a secondembodiment in another aspect of the present invention will be described.Note that description will be given below schematically to the extentnecessary to achieve the object of the present invention, thatdescription will be given mainly to the extent necessary to describeappropriate parts of the present invention, and that description relatedto known techniques will be omitted.

FIG. 20 is a block diagram of the osteoporosis diagnostic supportapparatus according to the second embodiment of the present invention.As shown in FIG. 20 , the osteoporosis diagnostic support apparatus 100includes a photographic apparatus 110 adapted to shoot subject images ofpatients or the like, an image analysis apparatus 120 adapted to analyzeimages shot by the photographic apparatus 110, and a display apparatus170 adapted to display the images shot by the photographic apparatus 110and information obtained by the image analysis apparatus 120, wherethese apparatus are linked together by wired and/or wirelessconnections.

The image analysis apparatus 120 includes a CPU 130, a memory 140, andinterfaces 150 and 160, which are linked, for example, as shown in FIG.20 . The memory 140 includes a contour extraction unit 141, a linesegment extraction unit 144, a mandibular cortical bone morphologicalindex identification unit 146, and an osteoporosis diagnostic supportunit 45.

A panoramic X-ray imaging apparatus, which is a type of the photographicapparatus 110, is an apparatus adapted to shoot panoramic images in thedental area with X-rays. Various types of panoramic X-ray imagingapparatus have been put to practical use and any of them may be adopted.Note that the photographic apparatus 110 is not limited to the panoramicX-ray imaging apparatus, and any of a usual X-ray imaging apparatus,MRI/CT imaging apparatus, ultrasound imaging apparatus or a combinationthereof may be adopted as the photographic apparatus 110. Appropriatediagnostic support may be provided by resulting images.

The panoramic image shot by the panoramic X-ray imaging apparatusserving as the photographic apparatus 110 is sent to the image analysisapparatus 120. The image analysis apparatus 120 analyzes images, beingprovided with computer resources including at least the CPU 130, memory140, interface 150 with the photographic apparatus 110, interface 160with the display apparatus 170 (described later). The computer resourcesmay be provided in the form of a server or personal computer installedin close proximity, similar apparatus linked by wired and/or wirelessconnections, or Internet-based cloud.

The display apparatus 170 is connected to the image analysis apparatus120 via the interface 160 and is capable of displaying the images shotby the photographic apparatus 110, images of a mandibular contour andline segments extracted by the image analysis apparatus 120, informationabout the morphological index and the like of the mandibular corticalbone identified by the image analysis apparatus 120, osteoporosisdiagnostic support information obtained by the image analysis apparatus120, and the like.

The contour extraction unit 141 is provided as a program stored in thememory 140 of the image analysis apparatus 120. The contour extractionunit 141 extracts a mandibular contour from a panoramic image. Themandibular contour is a portion which defines an outer edge of the lowerjawbone.

Also, the line segment extraction unit 144 is provided as part of theimage analysis apparatus 120. The line segment extraction unit 144,which is, for example, like a line-convergence index filter, is aprogram stored in the memory 140, and is capable of causing a computerto perform a function to extract line segments from a panoramic image.

Also, the mandibular cortical bone morphological index identificationunit 146 is provided as part of the image analysis apparatus 120. Themandibular cortical bone morphological index identification unit 146,which also is a program stored in the memory 140, identifies themorphological index of the mandibular cortical bone based on resultsproduced by either or both of the mandibular cortical bone contourextraction unit 141 and line segment extraction unit 144.

Furthermore, the osteoporosis diagnostic support unit 145 is provided aspart of the image analysis apparatus 120, allowing identificationresults produced by the mandibular cortical bone morphological indexidentification unit 146 to be compared with data stored in theosteoporosis diagnostic support database (not shown), which is part ofthe osteoporosis diagnostic support unit 145.

Now, operation of the osteoporosis diagnostic support apparatusconfigured as described above will be described. FIG. 21 is a flowchartof the osteoporosis diagnostic support apparatus according to the secondembodiment of the present invention.

<Image Input> (S110)

First, images of the lower jawbone and surroundings thereof are inputtedby being shot by a panoramic X-ray imaging apparatus, which is a type ofthe photographic apparatus 110. Here, details are similar to the imageshooting according to the first embodiment.

<Contour Tracking> (S120)

Next, the shot panoramic image is inputted to the image analysisapparatus 120, and the mandibular contour is tracked by the contourextraction unit 141, which is part of the image analysis apparatus 120.Here, details are similar to the contour extraction according to thefirst embodiment.

<Region-of-Interest Setting> (S130)

Next, using the shot image and extracted contour, first, the mandibularcortical bone morphological index identification unit 146 sets a regionof interest (ROI) containing the mandibular cortical bone. Here, detailsare similar to the determination of measurement reference points andacquisition of profiles according to the first embodiment, and the sizeof the region of interest is expanded to suite the identification of themandibular cortical bone morphological index. That is, regions measuring151 pixels wide along the mandibular contour (50 pixels from therespective measurement reference points in a medial direction and 100pixels in an opposite direction) and 100 pixels high from the mandibularcontour in the vertical direction are set, respectively, around theright and left measurement reference points as regions of interest. FIG.22 shows an explanatory diagram of how to set regions of interest. Notethat both height and width of 1 pixel correspond to 0.1 mm.

<Extraction of Linear Structures> (S140)

Then, using a line-convergence index filter, the mandibular corticalbone morphological index identification unit 146 extracts lines made upof linear structures (linear image formed by bone resorption) and a grayvalue peak of a dense cortical bone portion from the set regions ofinterest. Specifically, as described in the first embodiment, ridgelines are extracted and thinning and noise removal are performed as wellusing a line-convergence index filter, and then the ridge lines formedby gray value peaks of the cortical bone (ridge lines of the corticalbone) and the ridge lines formed by gray value peaks of coarse structure(ridge lines of coarse structure) are detected. Images of linearstructures after application of a line-convergence index filter areshown in FIG. 23 .

<Density-Based Region Estimation> (S150)

Next, the mandibular cortical bone morphological index identificationunit 146 regards a line existing at the lowermost end (on the mandibularcontour side) as a gray value peak of a dense cortical bone portion andestimates a region up to an upper end of the line to be a region of thedense cortical bone portion. Also, 151 profiles 100 pixels long in thevertical direction from a lower end of each region of interest isacquired. FIGS. 24A to 24B show how a profile is acquired. A profile 100pixels long is acquired as shown in FIG. 24A and graphically displayedas shown in FIG. 24B, where the ordinate represents the gray value andthe abscissa represents the lower end of each region of interest, i.e.,the distance from the contour. Then, as shown in FIG. 25 , a cubicpolynomial approximation curve Ap of each original profile Or isacquired, and inflection points An of the curve Ap are estimated tobelong to a boundary line between the entire cortical bone CB and thecancellous bone. Then, a region up to the boundary line between theentire cortical bone, excluding the region of the dense cortical boneportion DCB estimated earlier, and the cancellous bone is estimated tobe a region of a coarse cortical bone portion SCB. FIG. 26 showsestimation results of the dense and coarse regions (whose boundaries areindicated by alternate long and short dash lines in the figure) in theregion of interest estimated in this way.

<Feature Value Extraction> (S160)

Next, the mandibular cortical bone morphological index identificationunit 146 extracts feature values for use to identify the morphologicalindex (type I, type II, or type III). Regarding the feature values, thefollowing five types are used:

(1) the feature value of thickness,

(2) the number of pixels of line elements in a dense cortical boneregion,

(3) the number of pixels of line elements in a coarse cortical boneregion,

(4) the area of a coarse cortical bone region, and

(5) the ratio of the average concentration value of line elementsbetween the cortical bone regions.

Regarding the feature value of thickness, three measurement referencepoints each are set for a total of six points on the left and right ofthe lower jawbone to determine a general thickness of the cortical bone.As shown in FIG. 27 , first, one each of measurement reference points L1and R1 is set on the left and right, and then measurement referencepoints L2, L3, R2, and R3, two each on both sides, are set at intervalsof 101 pixels from the respective measurement reference points L1 and R1along the mandibular contour. A function equivalent to that of thecortical bone thickness calculation unit 42 according to the firstembodiment is incorporated in the mandibular cortical bone morphologicalindex identification unit 146 and used to measure the cortical bonethickness at each of the measurement reference points, and then theaverage value of measurement results at four locations are used as thefeature value of thickness by excluding minimum and maximum values fromsix measured values by taking error values into consideration.

Next, feature values (2) to (4) are calculated using the diagramgenerated in the density-based region estimation. Specifically, as shownin FIG. 28 , (2) the number of pixels of line elements in a densecortical bone region, (3) the number of pixels of line elements in acoarse cortical bone region, and (4) the area of a coarse cortical boneregion are determined.

Next, feature value (5) is the ratio of the average concentration valueof line elements between the dense and coarse cortical bone regions. Forexample, if the average concentration value of pixels containing lineelements in the dense cortical bone region is 3453 and a similar averageconcentration value in the coarse cortical bone region is 3212, theratio between the average concentration values is 0.93 (3212/3453).

Here, because the area of a coarse region varies with the extent ofcoarseness, the area of a coarse cortical bone region is considered tobe especially useful as a feature value for use to identify themorphological index (type I, type II, or type III).

<Cortical Bone Morphological Index Identification> (S170)

Next, the mandibular cortical bone morphological index identificationunit 146 identifies the form of the cortical bone based on the featurevalues. A support vector machine (SVM), which is one of identificationtechniques based on “supervised learning,” is used for theidentification. SVM is one of the learning models which has the bestidentification performance.

Specifically, as shown in FIG. 29 , when feature values (1) to (5) arefound, for example, for 63 cases (learning samples), and these cases areclassified into type I, type II, and type III of the morphological indexaccording to physician's diagnosis, a separating hyperplane with amaximized margin can be determined from the feature values by theapplication of SVM, making it possible to identify the morphologicalindex of a new case (unlearned sample) with high accuracy.

Note that although it has been stated in the above description that asupport vector machine (SVM), which is one of identification techniquesbased on “supervised learning,” is used for the identification of theform of the cortical bone, any other identification technique, suchRandom Forest, Boosting, or Neural Network, may be used rather than SVMas long as the morphological index can be identified appropriately basedon a large number of feature values.

<Presentation of Identification Results> (S180)

Finally, identification (classification) results of the morphologicalindex are presented on the display apparatus 170, and the osteoporosisdiagnostic support unit 145 supports physician's diagnosis ofosteoporosis.

Note that although it has been stated in the above description thatthere are five feature values, one or more of them may be used ratherthan all the five and furthermore that feature values may be usefullyadded to improve accuracy of identification.

In particular, there is a technique which uses texture analysis, and 88(4 angles×2 regions of interest×11 types) feature values made up ofcombinations of the following items were extracted based on thetechnique.

Distance: 5 pixels.

Angles: 0, 45, 90, and 135 degrees (angles used to create a densityco-occurrence matrix).

Regions of interest: entire cortical bone, and only coarse cortical boneregion.

Types: contrast, correlation, variance, entropy, sum entropy, inversedifference moment, sum average, sum variance, difference variance,difference entropy, angular second moment.

The effectiveness of these items was checked by determining coefficientsof correlation with bone density, and it was found that the following 14feature values were effective.

0-, 45-, 90-, or 135-degree variance of a coarse cortical bone region,

0-, 45-, 90-, or 135-degree difference variance of a coarse corticalbone region,

45-, 90-, or 135-degree difference entropy of a coarse cortical boneregion,

0-degree inverse difference moment of all cortical bone regions,

0-degree difference entropy of all the cortical bone regions,

0-degree difference variance of all the cortical bone regions.

Thus, in the cortical bone morphological index identification stepdescribed earlier, all or one or more of the 14 feature values may beused in addition to, or instead of, the five feature values describedearlier. It can be expected that this will improve the accuracy ofidentification.

Also, although it has been stated in the above description that themorphological index of the cortical bone is classification into type I,type II, or type III, bone density may be estimated alternatively.

Specifically, when SVM was applied to the learning samples andregression analysis was conducted using Leave-one-out using the 5feature values used earlier for morphological index identification andall or one or more of the 14 feature values as well as the bone densitymeasured by an existing bone density measurement such as DXA as learningsamples, a correlation was found with high reliability. Using this, bonedensity in a new case (unlearned sample) can be estimated with highreliability. Since density can be estimated at a specific numeric value,this will help greatly in providing support for physician's osteoporoticdiagnosis.

REFERENCE SIGNS LIST

-   1 Osteoporosis diagnostic support apparatus-   10 Photographic apparatus-   20 Image analysis apparatus-   30 CPU-   40 Memory-   41 Contour extraction unit-   42 Cortical bone thickness calculation unit-   43 Cortical bone coarseness calculation unit-   44 Line segment extraction unit-   45 Osteoporosis diagnostic support unit-   50 Interface-   60 Interface-   70 Display apparatus-   100 Osteoporosis diagnostic support apparatus-   110 Photographic apparatus-   120 Image analysis apparatus-   130 CPU-   140 Memory-   141 Contour extraction unit-   144 Line segment extraction unit-   145 Osteoporosis diagnostic support unit-   146 Cortical bone morphological index identification unit-   150 Interface-   160 Interface-   170 Display apparatus

What is claimed is:
 1. An osteoporosis diagnostic support apparatuscomprising: a contour extraction unit adapted to extract a mandibularcontour from an image of a mandibular cortical bone photographed by aphotographic apparatus adapted to photograph the mandibular corticalbone and surroundings thereof; a line segment extraction unit adapted toextract line segments from the image of the mandibular cortical bonephotographed by the photographic apparatus, where the line segments areformed by a gray level distribution and include line segments of thecortical bone and line segments of a coarsely structured portion; and amandibular cortical bone morphological index identification unit adaptedto extract a feature value based on the extracted mandibular contour andline segments and identify a mandibular cortical bone morphologicalindex as one of Types I, II, and III by determining a separatinghyperplane with a maximized margin from the feature value, the Type Ibeing characterized by smooth inside surfaces of the cortical bone onboth sides, the Type II being characterized by irregular inside surfacesof the cortical bone and linear absorption being observed in aneighborhood of an inner side inside the cortical bone, and the Type IIIbeing characterized in that advanced linear absorption as well asfractures of the cortical bone are observed over the entire corticalbone, wherein the feature value includes one or more of: a feature valueof the thickness of the cortical bone, the number of pixels of lineelements in a cortical bone region estimated to be dense when regionsclassified by density are estimated based on the extracted mandibularcontour and line segments, the number of pixels of line elements in acortical bone region estimated to be coarse in the estimation of theregions classified by density, area of the cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity, a ratio of average concentration value of line elements betweenthe cortical bone region estimated to be dense and the cortical boneregion estimated to be coarse in the estimation of the regionsclassified by density, variance of the cortical bone region estimated tobe coarse in the estimation of the regions classified by density,difference variance of the cortical bone region estimated to be coarsein the estimation of the regions classified by density, differenceentropy of the cortical bone region estimated to be coarse in theestimation of the regions classified by density, inverse differencemoment of all cortical bone regions estimated to be dense or coarse inthe estimation of the regions classified by density, difference entropyof all the cortical bone regions estimated to be dense or coarse in theestimation of the region classified by density, and difference varianceof all the cortical bone regions estimated to be dense or coarse in theestimation of the regions classified by density.
 2. The osteoporosisdiagnostic support program according to claim 1 further comprising: acortical bone thickness calculation unit adapted to calculate athickness of the cortical bone based on the extracted mandibular contourand line segments; and a cortical bone coarseness calculation unitadapted to calculate a coarseness of the cortical bone based on theextracted mandibular contour and line segments, wherein the corticalbone thickness calculation unit determines a measurement reference pointbased on the mandibular contour extracted by the contour extractionunit, acquires profiles from the determined measurement reference pointby establishing plural points at predetermined intervals around eachmeasurement reference point on the mandibular contour, acquiring aperpendicular line from each of the points to the mandibular contour,and finding gray values at predetermined intervals on the perpendicularline, the gray values being gained by determining pixel valuespixel-by-pixel on the perpendicular line and by being converted intoimages, selects an optimum group of the profiles based on the linesegments of the cortical bone and the line segments of the coarselystructured portion extracted by the line segment extraction unit, andthereby calculates the thickness of the cortical bone from the selectedoptimum group of the profiles, and wherein the cortical bone coarsenesscalculation unit determines a measurement reference point based on themandibular contour extracted by the contour extraction unit, acquiresprofiles from the determined measurement reference point by establishingplural points at predetermined intervals around each measurementreference point on the mandibular contour, acquiring a perpendicularline from each of the points to the mandibular contour, and finding grayvalues at predetermined intervals on the perpendicular line, the grayvalues being gained by determining pixel values pixel-by-pixel on theperpendicular line and by being converted into images, calculates anarea of the line segments of the coarsely structured portion extractedby the line segment extraction unit, and thereby calculates thecoarseness of the cortical bone.
 3. The osteoporosis diagnostic supportapparatus according to claim 2, wherein the determination of themeasurement reference point in the cortical bone coarseness calculationunit includes detecting a mandibular angle.
 4. The osteoporosisdiagnostic support apparatus according to claim 2, wherein the linesegment extraction unit uses a line convergence index filter inextracting the line segments.
 5. The osteoporosis diagnostic supportapparatus according to claim 2, wherein the determination of themeasurement reference point in the cortical bone thickness calculationunit includes detecting a mandibular angle.
 6. The osteoporosisdiagnostic support apparatus according to claim 2, wherein the linesegment extraction unit uses a line convergence index filter inextracting the line segments.
 7. The osteoporosis diagnostic supportapparatus according to claim 2, wherein the cortical bone thicknesscalculation unit determines a measurement reference point based on themandibular contour extracted by the contour extraction unit, acquiresprofiles from the determined measurement reference point by establishingplural points at predetermined intervals around each measurementreference point on the mandibular contour, acquiring a perpendicularline from each of the points to the mandibular contour, and finding grayvalues at predetermined intervals on the perpendicular line, the grayvalues being gained by determining pixel values pixel-by-pixel on theperpendicular line and by being converted into images, selects anoptimum group of the profiles by selecting a profile group whichmaximizes the sum total ED decrease widths, the decrease widths beingdetermined as a difference between the gray value at a search startpoint and the smallest gray value in a search range of the profile, andthereby calculates the thickness of the cortical bone from the selectedoptimum group of the profiles by determining the gradients Ai of theprofile at each of points beginning with a measurement start point,calculating an average Aave of only the gradients associated withdecreasing gray values, determining a point Tresult closest to Ts whichsatisfies the condition of Ai<Aave, and designating the distance fromthe measurement start point to Tresult as the thickness of the corticalbone.
 8. The osteoporosis diagnostic support apparatus according toclaim 1, wherein the mandibular cortical bone morphological indexidentification unit is an identification unit made up of a supportvector machine.
 9. The osteoporosis diagnostic support apparatusaccording to claim 1, wherein the mandibular cortical bone morphologicalindex identification unit has a bone density estimation function.
 10. Anosteoporosis diagnostic support program configured to make a computerfunction as: contour extraction means for extracting a mandibularcontour from an image of a mandibular cortical bone photographed by aphotographic apparatus adapted to photograph the mandibular corticalbone and surroundings thereof; line segment extraction means forextracting line segments from the image of the mandibular cortical bonephotographed by the photographic apparatus, where the line segments areformed by a gray level distribution and include line segments of thecortical bone and line segments of a coarsely structured portion; andmandibular cortical bone morphological index identification means forextracting a feature value based on the extracted mandibular contour andline segments and identifying a mandibular cortical bone morphologicalindex as one of Types I, II, and III by determining a separatinghyperplane with a maximized margin from the feature value, the Type Ibeing characterized by smooth inside surfaces of the cortical bone onboth sides, the Type II being characterized by irregular inside surfacesof the cortical bone and linear absorption being observed in aneighborhood of an inner side inside the cortical bone, and the Type IIIbeing characterized in that advanced linear absorption as well asfractures of the cortical bone are observed over the entire corticalbone, wherein the feature value includes one or more of: a feature valueof the thickness of the cortical bone, the number of pixels of lineelements in a cortical bone region estimated to be dense when regionsclassified by density are estimated based on the extracted mandibularcontour and line segments, the number of pixels of line elements in acortical bone region estimated to be coarse in the estimation of theregions classified by density, area of the cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity, a ratio of average concentration value of line elements betweenthe cortical bone region estimated to be dense and the cortical boneregion estimated to be coarse in the estimation of the regionsclassified by density, variance of the cortical bone region estimated tobe coarse in the estimation of the regions classified by density,difference variance of the cortical bone region estimated to be coarsein the estimation of the regions classified by density, differenceentropy of the cortical bone region estimated to be coarse in theestimation of the regions classified by density, inverse differencemoment of all cortical bone regions estimated to be dense or coarse inthe estimation of the regions classified by density, difference entropyof all the cortical bone regions estimated to be dense or coarse in theestimation of the region classified by density, and difference varianceof all the cortical bone regions estimated to be dense or coarse in theestimation of the regions classified by density.
 11. An osteoporosisdiagnostic support apparatus comprising: a contour extraction unitadapted to extract a mandibular contour from an image of a mandibularcortical bone photographed by a photographic apparatus adapted tophotograph the mandibular cortical bone and surroundings thereof; a linesegment extraction unit adapted to extract line segments from the imageof the mandibular cortical bone photographed by the photographicapparatus, where the line segments are formed by a gray leveldistribution and include line segments of the cortical bone and linesegments of a coarsely structured portion; and a mandibular corticalbone morphological index identification unit adapted to extract afeature value based on the extracted mandibular contour electronicallyreceived from said contour extraction unit and the line segmentselectronically received from said line segment extraction unit andidentify a mandibular cortical bone morphological index as one of TypesI, II, and III by determining a separating hyperplane with a maximizedmargin from the feature value used in providing support for theosteoporotic diagnosis, the Type I being characterized by smooth insidesurfaces of the cortical bone on both sides, the Type II beingcharacterized by irregular inside surfaces of the cortical bone andlinear absorption being observed in a neighborhood of an inner sideinside the cortical bone, and the Type III being characterized in thatadvanced linear absorption as well as fractures of the cortical bone areobserved over the entire cortical bone, wherein the feature valueincludes one or more of: a feature value of the thickness of thecortical bone, the number of pixels of line elements in a cortical boneregion estimated to be dense when regions classified by density areestimated based on the extracted mandibular contour and line segments,the number of pixels of line elements in a cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity, area of the cortical bone region estimated to be coarse in theestimation of the regions classified by density, a ratio of averageconcentration value of line elements between the cortical bone regionestimated to be dense and the cortical bone region estimated to becoarse in the estimation of the regions classified by density, varianceof the cortical bone region estimated to be coarse in the estimation ofthe regions classified by density, difference variance of the corticalbone region estimated to be coarse in the estimation of the regionsclassified by density, difference entropy of the cortical bone regionestimated to be coarse in the estimation of the regions classified bydensity, inverse difference moment of all cortical bone regionsestimated to be dense or coarse in the estimation of the regionsclassified by density, difference entropy of all the cortical boneregions estimated to be dense or coarse in the estimation of the regionclassified by density, and difference variance of all the cortical boneregions estimated to be dense or coarse in the estimation of the regionsclassified by density.